Lets have a look at the dataset and explore the rows, and variables and their distribution.

attach(police) # attaching data to the work space
dim(police) # getting the number of rows and columns

## [1] 2384   47

The data describes about 2384 incidents with available informations on 47 variables(columns) reported in various districts, divisions and beats.Lets have a look at the first 6 rows of the data

#getting first 6 rows of the data
head(police)

##   INCIDENT_DATE INCIDENT_TIME    UOF_NUMBER OFFICER_ID OFFICER_GENDER
## 1    OCCURRED_D    OCCURRED_T        UOFNum CURRENT_BA         OffSex
## 2    09-03-2016   04:14:00 AM         37702      10810           Male
## 3       3/22/16   11:00:00 PM         33413       7706           Male
## 4       5/22/16   01:29:00 PM         34567      11014           Male
## 5    01-10-2016   08:55:00 PM         31460       6692           Male
## 6    11-08-2016   02:30:00 AM  37879, 37898       9844           Male
##   OFFICER_RACE OFFICER_HIRE_DATE OFFICER_YEARS_ON_FORCE OFFICER_INJURY
## 1      OffRace           HIRE_DT    INCIDENT_DATE_LESS_     OFF_INJURE
## 2        Black        05-07-2014                      2             No
## 3        White        01-08-1999                     17            Yes
## 4        Black           5/20/15                      1             No
## 5        Black           7/29/91                     24             No
## 6        White        10-04-2009                      7             No
##            OFFICER_INJURY_TYPE OFFICER_HOSPITALIZATION SUBJECT_ID SUBJECT_RACE
## 1              OFF_INJURE_DESC              OFF_HOSPIT     CitNum      CitRace
## 2 No injuries noted or visible                      No      46424        Black
## 3                Sprain/Strain                     Yes      44324     Hispanic
## 4 No injuries noted or visible                      No      45126     Hispanic
## 5 No injuries noted or visible                      No      43150     Hispanic
## 6 No injuries noted or visible                      No      47307        Black
##   SUBJECT_GENDER SUBJECT_INJURY          SUBJECT_INJURY_TYPE
## 1         CitSex     CIT_INJURE             SUBJ_INJURE_DESC
## 2         Female            Yes      Non-Visible Injury/Pain
## 3           Male             No No injuries noted or visible
## 4           Male             No No injuries noted or visible
## 5           Male            Yes               Laceration/Cut
## 6           Male             No No injuries noted or visible
##   SUBJECT_WAS_ARRESTED SUBJECT_DESCRIPTION          SUBJECT_OFFENSE
## 1           CIT_ARREST          CIT_INFL_A               CitChargeT
## 2                  Yes   Mentally unstable                    APOWW
## 3                  Yes   Mentally unstable                    APOWW
## 4                  Yes             Unknown                    APOWW
## 5                  Yes FD-Unknown if Armed           Evading Arrest
## 6                  Yes             Unknown Other Misdemeanor Arrest
##   REPORTING_AREA BEAT SECTOR      DIVISION LOCATION_DISTRICT STREET_NUMBER
## 1             RA BEAT SECTOR      DIVISION         DIST_NAME      STREET_N
## 2           2062  134    130       CENTRAL               D14           211
## 3           1197  237    230     NORTHEAST                D9          7647
## 4           4153  432    430     SOUTHWEST                D6           716
## 5           4523  641    640 NORTH CENTRAL               D11          5600
## 6           2167  346    340     SOUTHEAST                D7          4600
##    STREET_NAME STREET_DIRECTION STREET_TYPE
## 1       STREET         street_g    street_t
## 2        Ervay                N         St.
## 3     Ferguson             NULL         Rd.
## 4 bimebella dr             NULL         Ln.
## 5          LBJ             NULL       Frwy.
## 6    Malcolm X                S       Blvd.
##   LOCATION_FULL_STREET_ADDRESS_OR_INTERSECTION LOCATION_CITY LOCATION_STATE
## 1                               Street Address          City          State
## 2                               211 N ERVAY ST        Dallas             TX
## 3                             7647 FERGUSON RD        Dallas             TX
## 4                             716 BIMEBELLA LN        Dallas             TX
## 5                               5600 L B J FWY        Dallas             TX
## 6                        4600 S MALCOLM X BLVD        Dallas             TX
##   LOCATION_LATITUDE LOCATION_LONGITUDE INCIDENT_REASON REASON_FOR_FORCE
## 1          Latitude          Longitude      SERVICE_TY       UOF_REASON
## 2         32.782205         -96.797461          Arrest           Arrest
## 3         32.798978         -96.717493          Arrest           Arrest
## 4          32.73971          -96.92519          Arrest           Arrest
## 5                                               Arrest           Arrest
## 6                                               Arrest           Arrest
##     TYPE_OF_FORCE_USED1 TYPE_OF_FORCE_USED2 TYPE_OF_FORCE_USED3
## 1            ForceType1          ForceType2          ForceType3
## 2 Hand/Arm/Elbow Strike                                        
## 3           Joint Locks                                        
## 4     Take Down - Group                                        
## 5        K-9 Deployment                                        
## 6        Verbal Command     Take Down - Arm                    
##   TYPE_OF_FORCE_USED4 TYPE_OF_FORCE_USED5 TYPE_OF_FORCE_USED6
## 1          ForceType4          ForceType5          ForceType6
## 2                                                            
## 3                                                            
## 4                                                            
## 5                                                            
## 6                                                            
##   TYPE_OF_FORCE_USED7 TYPE_OF_FORCE_USED8 TYPE_OF_FORCE_USED9
## 1          ForceType7          ForceType8          ForceType9
## 2                                                            
## 3                                                            
## 4                                                            
## 5                                                            
## 6                                                            
##   TYPE_OF_FORCE_USED10 NUMBER_EC_CYCLES FORCE_EFFECTIVE
## 1          ForceType10       Cycles_Num      ForceEffec
## 2                                  NULL             Yes
## 3                                  NULL             Yes
## 4                                  NULL             Yes
## 5                                  NULL             Yes
## 6                                  NULL         No, Yes

Lets explore the names of the variables in this dataset

#let's starts exploring the variables
for(i in 1:length(names(police))){
  cat(i,"\b)",names(police)[i],", ")
}

## 1 ) INCIDENT_DATE , 2 ) INCIDENT_TIME , 3 ) UOF_NUMBER , 4 ) OFFICER_ID , 5 ) OFFICER_GENDER , 6 ) OFFICER_RACE , 7 ) OFFICER_HIRE_DATE , 8 ) OFFICER_YEARS_ON_FORCE , 9 ) OFFICER_INJURY , 10 ) OFFICER_INJURY_TYPE , 11 ) OFFICER_HOSPITALIZATION , 12 ) SUBJECT_ID , 13 ) SUBJECT_RACE , 14 ) SUBJECT_GENDER , 15 ) SUBJECT_INJURY , 16 ) SUBJECT_INJURY_TYPE , 17 ) SUBJECT_WAS_ARRESTED , 18 ) SUBJECT_DESCRIPTION , 19 ) SUBJECT_OFFENSE , 20 ) REPORTING_AREA , 21 ) BEAT , 22 ) SECTOR , 23 ) DIVISION , 24 ) LOCATION_DISTRICT , 25 ) STREET_NUMBER , 26 ) STREET_NAME , 27 ) STREET_DIRECTION , 28 ) STREET_TYPE , 29 ) LOCATION_FULL_STREET_ADDRESS_OR_INTERSECTION , 30 ) LOCATION_CITY , 31 ) LOCATION_STATE , 32 ) LOCATION_LATITUDE , 33 ) LOCATION_LONGITUDE , 34 ) INCIDENT_REASON , 35 ) REASON_FOR_FORCE , 36 ) TYPE_OF_FORCE_USED1 , 37 ) TYPE_OF_FORCE_USED2 , 38 ) TYPE_OF_FORCE_USED3 , 39 ) TYPE_OF_FORCE_USED4 , 40 ) TYPE_OF_FORCE_USED5 , 41 ) TYPE_OF_FORCE_USED6 , 42 ) TYPE_OF_FORCE_USED7 , 43 ) TYPE_OF_FORCE_USED8 , 44 ) TYPE_OF_FORCE_USED9 , 45 ) TYPE_OF_FORCE_USED10 , 46 ) NUMBER_EC_CYCLES , 47 ) FORCE_EFFECTIVE ,

Here when I look at the data I found that some of the variables are in wrong format.

for(i in 1:length(colnames(police))){
  cat(class(colnames(police)[i]),"\b, ")
}

## character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character , character ,

Here we can see that all variables are marked as a charactor datatype.But we can understand from data that the “INCIDENT_DATE”,“OFFICER_HIRE_DATE”,“INCIDENT_TIME” are in date time datatype and “OFFICER_YEARS_ON_FORCE” is numeric. So we can convert this into its actual datatype.

Now lets have look at the overall summary of the dataset.

#have a look at the summary of the data
# print(summary(police))
library(summarytools)
view(dfSummary(police))

We found that most of the variables are categorical variables. Lets have a look at the levels of some variables

#Analysing the various levels and frequency of the categorical variable

print(".........................................................................")

## [1] "........................................................................."

table(OFFICER_GENDER)

## OFFICER_GENDER
## Female   Male OffSex 
##    240   2143      1

print(".........................................................................")

## [1] "........................................................................."

table(OFFICER_RACE)

## OFFICER_RACE
## American Ind        Asian        Black     Hispanic      OffRace        Other 
##            8           55          341          482            1           27 
##        White 
##         1470

print(".........................................................................")

## [1] "........................................................................."

table(SUBJECT_GENDER)

## SUBJECT_GENDER
##  CitSex  Female    Male    NULL Unknown 
##       1     440    1932      10       1

print("..........................................................................")

## [1] ".........................................................................."

table(SUBJECT_RACE)

## SUBJECT_RACE
## American Ind        Asian        Black      CitRace     Hispanic         NULL 
##            1            5         1333            1          524           39 
##        Other        White 
##           11          470

From the above table we found that there are some irrelevent levels in the variables I would prefer to eliminate all those for a convinient visualization.

This elimination create some na values in the dataset. Lets calculate the sum of na values in this dataset and we can remove the the na values in one go if removing in one go doesnt affect the data that much here in this case we can do that but in some cases it is not recommmended since especially dealing with numerical variables because we may loose our acuracy in prediction.

sum(is.na(police)) # calculating total na values

## [1] 92

police <- na.omit(police) # removing na values

Here we found that there are total 92 na values and I decide to remove that.

Lets starts exploring the data using visualization

The plot displays the incidents across districts and divisions.This effectively conveys that most of the incidents are reported in D14 and D2 districts and most of them are in cental division.

This plot conveys the incidents reported in various location States and Streets

Lets have a look at the officers in force based on their gender and race, and also the subjects involved in the incidents based on their gender and race.

The plot shows that the White male officers are more in force. And male subjects belonging to the Black ethnic group has involved more in the incidents.

This plot Shows that more officers in force has an experience of 2 years and the majority of the police officers has less than 10 years of experience.

This plot shows that male officers are more likely to use the forces during the incidents.

And most of the cases they have used verbal command and weapon display in order to comply.

Plot shows that White officers are more likely to use the force and in most of the cases the reason for the force is arrest.

### This plot displays the incidents happened across various beats,streets and sector

This plot shows that White male officers are more likely to get injured duting the incidents whereas the Black males are more likely to get injured during the incidents.

These plots shows that more subjects are arrested by the officers with less than 10 yeras of experience in force.And white officers have arrested and injured more number of black ethnic group during the incidents.Male officers have arrested and injured the subjects more compared to female officers.